Predictive audiences: leverage first-party data for prospecting and retargeting

Predictive audiences: leveraging first-party data for prospecting and retargeting 

Today, more than 10 million advertisers on Meta and over 8 million on Google compete for the buyers' attention. Success on these advertising platforms has become increasingly costly to achieve, especially since marketers must also minimize iOS 14+ impact on ad performance. To reduce the costs of campaigns, marketers have limited options, including optimizing creatives and segmentation. The rising cost of advertisement has been a prerogative to search for alternatives to improve ad performance and thus, reduce costs. AI-based predictive audiences represent the solution that harnesses the power of first-party data to improve ad efficiency. 

What are Admetrics’ AI-based predictive audiences?

Predictive audiences use artificial intelligence to analyze website behavior and customer interactions and identify patterns based on which customer segments are created and classified as having a high, medium, or low purchasing intention. These segments can be used for retargeting or seeding lookalike models for prospecting new customers.


How predictive audiences work

Predictive audiences use first-party data to anticipate the likelihood of a prospect converting to a certain action. They can be used to retarget only visitors that have a high buying intent or prospect new customers by replicating successful campaigns. 

What is lookalike modeling?

Lookalike audiences are based on historical data used to determine segments that are identical to previous successful audiences. Based on first-party data, they highlight the demographics, preferred advertising channels, and other specific traits of individuals that engaged with a campaign or converted according to particular goals. This data is then used to reach new audiences with the same characteristics. 

Modeling lookalike audiences will need extensive data sets to find matching potential customers. Also, the historic data refers to buying behaviors of previous customers, which in time can change. This translates into reactive marketing efforts and, for example, will hinder the process of prospecting audiences for newly launched products.


How predictive audiences improve lookalike modeling

Our AI-generated predictive audiences provide an efficient way of reaching potential customers with a high probability of buying. By using the predictive audiences to seed your lookalike models, we can increase reach and performance of prospecting campaigns drastically. 

Also, since Admetrics' models' actions users might  take in the future, it eliminates the need for large historical data sets. 

Using predictive audiences for retargeting

The Admetrics AI-driven predictive audiences solution will analyze different factors such as visitors' on-site behavior and their interaction with certain products or services offered and predict their likelihood to convert. This allows marketers to implement a conversion-centric retargeting approach based on a perfect blend of browsing and purchase behavior. The granular focus helps improve ad efficiency by targeting only prospects with a high likelihood of purchasing.

Benefits of predictive audiences

Predictive audience data can then be extended to create lookalike models, streamline new customer acquisition campaigns, and significantly reduce acquisition costs.  

Behavioral customer data based on website interaction powers predictive audiences which brings a multitude of advantages. Making use of the already available first-party data is the fastest and most reliable way of harvesting great benefits like higher return on investment, lower costs per acquisition, and increased return on ad spend(ROAS). 

Admetrics' AI-driven marketing platform implements predictive audiences, which help predict customer behavior, improve ad relevancy, foster higher engagement, and require no human interaction while reducing customer acquisition and retargeting costs.  


Predicting customers behavior 

In predictive audiences, the forecast model determines the likelihood of selling products or services to a specific customer segment, thus understanding customers' behavior. For this, historical data such as purchasing activity and online behavior like metrics from social channels and e-commerce websites are analyzed. This helps marketers understand customers' buying intent, their affinity for a certain product or service and implement an omnichannel marketing strategy. Based on this data, marketers have a substantial benefit in crafting successful campaigns and marketing the right products to the best audience. 


Improved ad relevancy  

With AI-driven predictive audiences, marketers can improve the relevancy of ads served by crafting a data-driven outreach to a specific segment of potential customers that show the highest purchase intent. 

The success of a stellar marketing campaign is highly dependent on the chosen audience. Even if a campaign is crafted with a customer-centric approach, retargeting visitors that lack an increased purchasing affinity can be redundant and cost significantly more.

Better engagement 

Showing relevant ads to interested prospects is the perfect match, and implementing predictive audiences can help improve engagement with ads and campaigns. Overall interaction such as click-through rates, conversion rates, and other performance indicators go up. This ultimately translates into better campaign results that marketers can replicate with similar high-value customer segments. 

No human interaction requiered

The predictive audiences use artificial intelligence to analyze considerable data sets from the website interaction, identify patterns, and assess buying intent. This happens in the background, and the feature can be initially set up in minutes.


Reduced customer acquisition and retargeting costs 

By implementing Admetrics predictive audiences feature, marketers can retarget only high-value customer segments. These are the most likely to convert, while other website visitors are simply removed from the retargeting campaigns. By targeting only visitors likely to convert, Admetrics clients generate 30% more sales at the same ad spend. 

Costs are also reduced for the prospecting campaigns. Marketers can boost new customer acquisitions through high-impact audiences. Lookalike audiences seeded through Admetrics Predictive Audiences outperform other prospecting campaigns by >30%. 

Lookalike audiences seeded through Admetrics Predictive Audiences outperform other prospecting campaigns by >30%. 

Get started with predictive audiences

Predictive audiences are an easy way to harness the power of first-party data and leverage its advantages to predict visitors' behavior and create relevant engagement with prospects and customers. By using Admetrics Data Studio, marketers can create cost-effective retargeting and acquisition campaigns that will proactively advance a customer-centric approach and ultimately increase ad relevancy and performance for better ROAS. 


To see how you can leverage the predictive audiences feature of Admetrics Data Studio, get in touch with one of our product specialists.